The last development in the IoT related field in conjunction with the lowering of the costs of the consumer electronics have led to an uncontrolled growth in the number of devices, technology systems and software applications able to transfer and receive data. The recent technological evolution has impacted also the healthcare and pharmaceutical related systems which should daily face with the variability of devices, standards and flows of data.

QMP is a remote monitoring platform able to manage background acquisition of 24/7 data related to behavioral and physical habits through the analysis of data detected by both smartphone and wearable devices. The users’ behavior is transparently monitored by exploiting the sensors and data flows of their own smartphones. By design, QMP does not provide user any feedback and does not introduce any burden other than carrying a phone.

The entire development process of QMP is based on a human-centered design philosophy aimed at encountering our customers’ needs which are translated into tailored features and tools. No prior technical knowledge is required to fully exploit the potentialities of our products while we make sure your data management is compliant with all relevant regulations, such as ICH-GCP and GDPR.


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The monitoring kernel of QMP implements the smartphone passive-sensing approach to provide a transparent tool for continuous monitoring in a free-living context. The monitoring platform takes advantages of the embedded sensors and registries of the smartphone to collect data about the physical activity (IMUs), mobility habits (GPS), smartphone usage (social interaction registries, smartphone uptime, calls registries) and heart related parameters (PPG sensor of the connected smartwatch). The data acquisition is performed in background on the user smartphone without affecting the performance or functionalities of the device and without the need of user interaction. The acquired data are automatically uploaded on the remote repository and made available for the subsequent data processing. The data, acquired daily by the monitoring app, are processed to detect any variation from user’s typical behavior.

Inferring new knowledge from data is at the core of Ab.Acus’ mission. Recent improvements in artificial intelligence and machine learning techniques opened up to a widespread and systematic use of data in defining new research and innovation path in healthcare. QMP platform integrates a data processing tool that exploits the last findings in Machine Learning and Artificial Intelligence to extract and manage high quality and reusable data.

The data processing tool makes available the following functionalities:

  • Extraction of innovative features and descriptors starting from raw sensor data
  • Unsupervised statistical data analytics and Machine Learning-based data processing
  • Data harmonization of different datasets and sensor data
  • Extraction of indices and scores based on the last data-fusion techniques
  • Management of baseline and training datasets for supervised data analytics

Ab.Acus dashboard is a flexible, fully customizable and web-based platform that enables clinicians and researchers to easily and safely manage their data. Thanks to the expertise of our engineers, every customer may build their own personalized dashboard exploiting the potentialities given by the modular architecture of our platform.

With the Ab.Acus dashboard you can:

  • Easily and safely manage your patient database
  • Visualize the stats about your data through interactive and AI-drive graphs
  • Manage user roles and access ensuring the highest level of privacy
  • Create workgroups to manage multicentric applications
  • Print personalized reports

The Ab.Acus dashboard offers healthcare professionals and researchers the information they need to follow up their patients’ evolution, adherence to medication, cognitive and physical status, etc. The most innovative data visualization techniques will help you to easily read big amount of data and to have an always updated view of datasets status at a glance.


  1. Simonetti V, Baccinelli W, Bulgheroni M and d’Amico E, Free context smartphone based application for motor activity levels recognition, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 7-9 September 2016, Bologna, 978-1-5090-1131-5/16 ©2016 IEEE 10.1109/RTSI.2016.7740601
  2. Tonti S, Marzolini B, Bulgheroni M, Validation of a cloud based ecosystem for behavioral and physical monitoring in free-living context. JMIR Preprints. 10/07/2019:15417